import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

A						=			logging.getLogger()


def 	lowerCamelCase					(	)   ->    List[Any]:
		_lowerCamelCase				   =  argparse.ArgumentParser()
		parser.add_argument('-f'							)
		_lowerCamelCase				   =  parser.parse_args()
		return args.f


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    Tuple:
		_lowerCamelCase				   =  {}
		_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       'all_results.json'							)
		if os.path.exists(UpperCamelCase							):
				with open(UpperCamelCase			,       'r'							) as f:
						_lowerCamelCase				   =  json.load(UpperCamelCase							)
		else:
				raise ValueError(F"""can't find {path}"""							)
		return results


def 	lowerCamelCase					(	)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.cuda.is_available() and torch_device == 'cuda'
		return is_using_cuda and is_apex_available()


A						=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@classmethod
		def   _snake_case       (       cls     :     Any		)      ->	Tuple:
				# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
				_lowerCamelCase				   =  tempfile.mkdtemp()
				_lowerCamelCase				   =  os.path.join(cls.tmpdir     ,  'default_config.yml'		)
				write_basic_config(save_location=cls.configPath		)
				_lowerCamelCase				   =  ['accelerate', 'launch', '--config_file', cls.configPath]
		@classmethod
		def   _snake_case       (       cls     :     Optional[int]		)      ->	int:
				shutil.rmtree(cls.tmpdir		)
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     Any		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

				if is_cuda_and_apex_available():
						testargs.append('--fp16'		)

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertGreaterEqual(result['eval_accuracy']     ,  0.75		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'epoch_0'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'glue_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     List[Any]		)      ->	int:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

				if torch.cuda.device_count() > 1:
						# Skipping because there are not enough batches to train the model + would need a drop_last to work.
						return

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertLess(result['perplexity']     ,  1_0_0		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'epoch_0'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'clm_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	Optional[Any]:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertLess(result['perplexity']     ,  4_2		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'epoch_0'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'mlm_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	List[str]:
				# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
				_lowerCamelCase				   =  7 if get_gpu_count() > 1 else 2

				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertGreaterEqual(result['eval_accuracy']     ,  0.75		)
				self.assertLess(result['train_loss']     ,  0.5		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'epoch_0'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'ner_no_trainer'		)		)		)
		@unittest.skip(reason='Fix me @muellerzr'		)
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	List[str]:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
				self.assertGreaterEqual(result['eval_f1']     ,  2_8		)
				self.assertGreaterEqual(result['eval_exact']     ,  2_8		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'epoch_0'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'qa_no_trainer'		)		)		)
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     Any		)      ->	Any:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertGreaterEqual(result['eval_accuracy']     ,  0.8		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'swag_no_trainer'		)		)		)
		@slow
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	List[Any]:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertGreaterEqual(result['eval_rouge1']     ,  1_0		)
				self.assertGreaterEqual(result['eval_rouge2']     ,  2		)
				self.assertGreaterEqual(result['eval_rougeL']     ,  7		)
				self.assertGreaterEqual(result['eval_rougeLsum']     ,  7		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'epoch_0'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'summarization_no_trainer'		)		)		)
		@slow
		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertGreaterEqual(result['eval_bleu']     ,  3_0		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'epoch_0'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'translation_no_trainer'		)		)		)
		@slow
		def   _snake_case       (       self     :     int		)      ->	Tuple:
				_lowerCamelCase				   =  logging.StreamHandler(sys.stdout		)
				logger.addHandler(snake_case__		)

				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				self.assertGreaterEqual(result['eval_overall_accuracy']     ,  0.10		)



		@mock.patch.dict(os.environ     ,  {'WANDB_MODE': 'offline'}		)
		def   _snake_case       (       self     :     Any		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

				if is_cuda_and_apex_available():
						testargs.append('--fp16'		)

				run_command(self._launch_args + testargs		)
				_lowerCamelCase				   =  get_results(snake_case__		)
				# The base model scores a 25%
				self.assertGreaterEqual(result['eval_accuracy']     ,  0.6		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'step_1'		)		)		)
				self.assertTrue(os.path.exists(os.path.join(snake_case__     ,  'image_classification_no_trainer'		)		)		)
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()